
Comparing the results of Base and (a) in Table REF , we can find that the incorporating Posterior Knowledge Explorer (PoKE) substantially boosts the performance of base model, e.g., 0.261 \(\rightarrow \)  0.285 in CIDEr score. More encouragingly, the “Base w/ PoKE” even achieves competitive results with the state-of-the-art models on IU-Xray dataset (see Table REF ), which demonstrates the effectiveness of our PoKE. We hypothesize that this performance gain may due to that PoKE can provide more accurate abnormal visual regions, which alleviate the visual data deviation problem. To verify this hypothesis, following [1]}, [2]}, we randomly select five abnormality categories, i.e., `Emphysema', `Pneumonia', `Cardiomegaly', `Pneumothorax' and `Lesion' from the IU-Xray dataset, to test the models' ability of detecting abnormalities. As we can see, Table REF  proves our argument and verifies that PoKE can better recognize abnormalities which is important in clinical diagnosis.
